tp030ny's repositories
positional_cl
code for paper Positional Contrastive Learning for Volumetric Medical Image Segmentation
ANN2SNNConversion_SNM_NeuronNorm
Pytorch Implementation of Signed Neuron with Memory: Towards Simple, Accurate and High-Efficient ANN-SNN Conversion, IJCAI 2022
Awesome-Diffusion-Models-in-Medical-Imaging
Diffusion Models in Medical Imaging
awesome-spiking-neural-networks
A curated list of materials for Spiking Neural Networks, 3rd generation of artificial neural networks.
Awesome-Weak-Shot-Learning
A curated list of papers, code and resources pertaining to weak-shot classification, detection, and segmentation.
bindsnet
Simulation of spiking neural networks (SNNs) using PyTorch.
brian2
Brian is a free, open source simulator for spiking neural networks.
brian2modelfitting
Model fitting toolbox for the Brian 2 simulator
CLIP
CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image
direct-training-snn
This project used STBP-tdBN method to directly train Deep Spiking Neural Networks from scratch with PyTorch
DSR
[CVPR 2022] Training High-Performance Low-Latency Spiking Neural Networks by Differentiation on Spike Representation
gpt_academic
为ChatGPT/GLM提供实用化交互界面,特别优化论文阅读/润色/写作体验,模块化设计,支持自定义快捷按钮&函数插件,支持Python和C++等项目剖析&自译解功能,PDF/LaTex论文翻译&总结功能,支持并行问询多种LLM模型,支持chatglm2等本地模型。兼容文心一言, moss, llama2, rwkv, claude2, 通义千问, 书生, 讯飞星火等。
simCLR-snn
PyTorch implementation of SimCLR by snn: A Simple Framework for Contrastive Learning of Visual Representations
SNN-event-driven-learning
An event-driven learning algorithm for spiking neural networks
SNN_CV_Applications_Resources
Paper list for SNN based computer vision tasks.
snn_optimal_conversion_pipeline
Optimal Conversion of Conventional Artificial Neural Networks to Spiking Neural Networks
snn_toolbox
Toolbox for converting analog to spiking neural networks (ANN to SNN), and running them in a spiking neuron simulator.
snntorch
Deep and online learning with spiking neural networks in Python
Spike-Element-Wise-ResNet
Deep Residual Learning in Spiking Neural Networks
Spiking-Neural-Network-SNN-with-PyTorch-where-Backpropagation-engenders-STDP
What about coding a Spiking Neural Network using an automatic differentiation framework? In SNNs, there is a time axis and the neural network sees data throughout time, and activation functions are instead spikes that are raised past a certain pre-activation threshold. Pre-activation values constantly fades if neurons aren't excited enough.
spikingjelly
SpikingJelly is an open-source deep learning framework for Spiking Neural Network (SNN) based on PyTorch.
SSL-MedSeg
Official implementation of our paper "Self-Supervised Pretraining for 2D Medical Image Segmentation"
SSL_medicalimaging
Codebase for Imperial MSc AI Group Project: How Well Do Self-Supervised Models Transfer to Medical Imaging?
STBP-simple
A simple direct training implement for SNNs using Spatio-Temporal Backpropagation
STDP-based-DCNN
Reimplementation of the paper "STDP-based spiking deep convolutional neural networks for object recognition"
Supervised-SNN-with-GD
A supervised learning algorithm of SNN is proposed by using spike sequences with complex spatio-temporal information. We explore an error back-propagation method of SNN based on gradient descent. The chain rule proved mathematically that it is sufficient to update the SNN’s synaptic weights by directly using an optimizer. Utilizing the TensorFlow framework, a bilayer supervised learning SNN is constructed from scratch. We take the lead in the application of SAR image classification and conduct experiments on the MSTAR dataset.
temporal_efficient_training
Code for temporal efficient training